tbuckley/cabot-search huggingface dataset:
The percentage of unnecessary mastectomy due to false size prediction by preoperative imaging studies in patients with breast cancer who underwent neoadjuvant chemotherapy.
Authors: Yireh Han, Wonshik Han
Journal: Journal of Clinical Oncology (2022)

573 Background: It was shown that, with neoadjuvant chemotherapy (NCT), about 40-50% of breast conserving surgery (BCS) ineligible breast cancer converted to BCS-eligible. Because the estimated tumor extent with imaging after NCT tend to be discordant with pathologic extent, we assumed that proportion of unnecessary total mastectomy (TM) might be greater in NCT patients compared with non-NCT patients. We aimed to determine the proportion of TM for potential BCS candidate due to false size prediction after NCT and factors affecting that. Methods: We prospectively enrolled patients with invasive breast cancer (Stage II or III) who were scheduled for TM from 2018 to 2021 at Seoul National University Hospital (SNUH). Patients who were determined to undergo TM by only patient’s preference, who had BRCA1/2 mutation, multicentric cancer, diffuse malignant calcification over more than a quadrant, and inflammatory cancer were excluded. The decision of TM was done by operating surgeon considering the tumor extent, location, and breast size. Breast imaging including mammography, ultrasonography (USG), and MRI were done in patients. Before surgery, each surgeon recorded a hypothetic maximum tumor size (HMS) that the surgeon would have been able to try BCS if the patient had actually less than that size of tumor at that location in the patient. After operation, the HMS was compared with final pathologic total extent of the tumor including invasive and in situ cancer. In SNUH, decisions and tries of BCS led to eventual BCS with negative margin in 97.4% of patients during same period with this study. Results: Among 360 patients enrolled, 130 were patients who underwent NCT, and 230 were chemotherapy naïve as a control group. 62 of 130 (52.3%) in the NCT group and 49 of 230 (21.3%) in the non-NCT group were found to have smaller tumor extent than the pre-recorded HMS, respectively (NCT vs non-NCT p-value &lt;0.001). Further analyses were done only for NCT group. In an analysis according to subtype, the proportion of TM with false size prediction were more in HER2-positve (63.3%) and triple-negative breast cancer (TNBC) (57.6%) than in ER-positive/HER2-negative breast cancer (25.0%) (p-value &lt;0.001). In an imaging analysis, MRI size were larger than HMS in 73.8% of all patients. Both MRI-pathology size and USG-pathology size discrepancies were significantly associated with false decision of TM (both p&lt;0.001), but not with mammography. Without MRI, the false decision would have been reduced by 26.1%. Conclusions: We found that 52.3% of patients who received TM after NCT were actually BCS eligible, which was significantly higher than non-NCT patients. The proportion of false prediction were more in HER2-positive and TNBC. MRI imaging contributed to false size prediction and subsequent TM for BCS eligible patients. Clinical trial information: NCT04689529.
Google search engine analytics for cancer immunotherapy: What patients are searching for on the internet and where they find answers.
Authors: Neeraj Suresh, Karthik Rajasekaran
Journal: Journal of Clinical Oncology (2022)

11036 Background: Immunotherapy in the treatment of cancer has been on the rise and is on the forefront of current cancer research. As such, patients are increasingly turning to the Internet and online resources to answer their questions. For any given Google search, artificial intelligence and machine learning algorithms generate a “People Also Ask” (PAA) section which returns the most commonly associated questions to the original search query, with corresponding websites to answer those questions. Using a search engine optimization (SEO) tool, we determined the most frequently searched terms and questions regarding cancer immunotherapy online, and the websites used to answer them. Methods: The top Google search queries and PAA questions with associated volume metrics were extracted for the terms “immunotherapy” and “cancer immunotherapy” using an SEO tool. PAA questions and corresponding websites were categorized by specific topic and Rothwell’s classification system. Websites were further quality assessed using the Journal of the American Medical Association (JAMA) benchmark criteria. Results: Global searches for cancer immunotherapy increased significantly each year since 2015. Countries with the most search volume were the U.S. (44%), the U.K. (12%), and India (11%). PAAs (n = 674) and the most frequently searched questions regarding cancer immunotherapy, by average monthly search volume (searches per month), were on: definitions/how it works (n = 6.6K, 21.3%), side effects (n = 4.4K, 14.2%), cost (n = 2.9K, 9.4%), immunotherapy vs. chemotherapy (n = 1.8K, 5.8%), drug options (n = 1.7K, 5.5%), signs immunotherapy is working (n = 1.2K, 3.9%), different types of immunotherapy (n = 1.1K, 3.5%), route of administration (n = 800, 2.6%), efficacy/success rate (n = 800, 2.6%), and effect on life expectancy (n = 300, 1.0%). The top cancers that patients searched immunotherapy treatments for were: lung (n = 1.9K, 23.2%), melanoma (n = 1.5K, 18.3%), pancreatic (n = 700, 8.5%), breast (n = 600, 7.3%), and bladder (n = 500, 6.1%). The most queried immunotherapy drugs were Pembrolizumab/Keytruda (n = 87K), Nivolumab/Opdivo (n = 33K), and Ipilimumab/Yervoy (n = 11.1K). Websites used to answer PAA questions were mostly those of academic institutions (n = 294, 43.6%), commercial organizations (n = 167, 24.8%), and government databases (n = 126, 18.7%). Conclusions: The field of cancer immunotherapy is rapidly evolving and patients often look to the Internet to answer their latest questions. Using our findings, academic institutions, commercial organizations, and physicians involved in multi-disciplinary cancer care can cater their patient communications and websites to more directly align with patients’ most common questions and concerns on immunotherapy.
An extracellular vesicle-based assay for noninvasive detection of metastases and monitoring prostate cancer.
Authors: Jasmine Jiemei Wang, Na Sun, Yi-Te Lee, Minhyung Kim, Sungyong You, Dolores Di Vizio, Hsian-Rong Tseng, Jie-Fu Chen, Yazhen Zhu, Edwin Melencio Posadas
Journal: Journal of Clinical Oncology (2022)

e17004 Background: Optimizing outcomes in prostate cancer (PCa) requires timely detect of metastatic progression of prostate cancer patients. We aim to optimize an extracellular vesicle (EV)-based Digital Scoring Assay for detecting PCa metastasis and noninvasive monitoring disease progression. Methods: PCa EV Digital Scoring Assay was developed by combining EV Click Chip, a nanotechnology-enabled microfluidic device for purifying PCa-derived EV and reverse-transcription droplet digital PCR for profiling disease-relevant mRNAs. A panel of 11 PCa-relevant mRNA markers were selected from The Tissue Atlas and a blood-based biomarker study. Plasma samples from 20 localized, 20 metastatic PCa patients, and serial plasma from three PCa patients were used to assess the performance of PCa EV Digital Scoring Assay with the 11-gene panel. mRNA expression of the panel was computed using weighted Z score method. ROC analysis was applied to discriminate localized from metastatic PCa. Results: After optimization, the capability of EV Click Chips (92.5 ± 5.4%) for capturing PCa-derived EVs from artificial plasma samples spiked with EVs outperformed ultracentrifugation (41.0 ± 3.2%) or precipitation methods (34.9± 2.2%; P&lt; 0.001). Strong signals of the 11-gene panel, i.e., ACP3, FOLH1, HOXB13, KLK2, KLK3, KLK4, MSMB, RLN1, SLC45A3, STEAP2, and TMPRSS2, were detected from three PCa cells (LNCaP, C4-2B, and 22Rv1) and their derived EVs, while rare signals were detected in either WBCs or EVs purified from male healthy donor. In the clinical study, the Z scores calculated from the 11-gene panel were significantly higher in metastatic than localized PCa with an area under the ROC curve of 0.88 (95% CI: 0.78-0.98). Finally, longitudinal analyses of three PCa patients with clinical progression, response, and stable diseases, respectively, showed the Z scores can precisely reflect the disease status even when it is undetectable by imaging. Conclusions: We demonstrate a sensitive PCa EV Digital Scoring Assay to identify metastatic PCa and dynamically monitor disease states in a noninvasive manner. This assay may augment current imaging tools for timely detection of PCa progression and provide a means to better personalized care.
Robust artificial intelligence-powered imaging biomarker based on mammography for risk prediction of breast cancer.
Authors: Eun Kyung Park, Hyeonsoo Lee, Minjeong Kim, Ki Hwan Kim, Hyeonseob Nam, Yoosoo Chang, Seungho Ryu
Journal: Journal of Clinical Oncology (2022)

10533 Background: Accurate risk assessment allows the precise personalized screening of breast cancer. Conventional statistical risk models have been used that estimate the probability of breast cancer incidence with patient demographics, detailed personal and family history. The purpose of this study was to investigate the feasibility of the AI-powered Imaging Biomarker in mammography (IBM) which was developed to predict the risk of future breast cancer beyond the mammographic breast density. Methods: We trained and developed the deep learning risk model, the AI-powered IBM, using a total of 36,955 examinations from 21,438 patients, who underwent at least one mammogram using Hologic or Siemens machines and pathology-confirmed breast cancer outcomes. To discover the feasibility of the AI-powered IBM, mammograms and various clinical information including pathology-confirmed breast cancer outcomes were collected, which were only used for external validation. C-indices and receiver operating characteristic (ROC) curves for 1- to 5-year outcomes were obtained. We compared 5-year ROC area under the curves (AUCs) of our AI-powered IBM and statistical risk models including the Tyrer-Cuzick model and the Gail model, which were most commonly used and widely known, using DeLong’s test. Results: A total of 16,894 mammograms were collected for external validation, of which 4,002 were followed by a cancer diagnosis within 5 years. Our AI-powered IBM obtained C-index of 0.758, and the model demonstrated the risk of breast cancer with AUC of 0.895 (95% CI: 0.880, 0.909) at 1-year, 0.839 (0.824, 0.852) at 2-year, 0.807 (0.794, 0.819) at 3-year, 0.783 (0.7947, 0.819) at 4-year. The 5-year AUC of our AI-powered IBM was 0.808 (0.792, 0.822). Our AI-powered IBM showed significantly higher 5-year AUC than the Gail model (AUC: 0.572, P&lt; 0.001) and the Tyrer-Cuzick model (0.569, P&lt; 0.001). Conclusions: A deep learning AI-powered IBM using mammograms has a substantial potential to advance toward the robust risk prediction of breast cancer over conventional risk models. This approach for risk stratification of breast cancer might be feasible to improve personalized screening programs.
Phase II study of disulfiram (D) and cisplatin (P) in refractory germ cell tumors (GCTs).
Authors: Michal Mego, Daniela Svetlovska, Silvia Schmidtova, Katarina Kalavska, Jana Obertova, Patrik Palacka, Valentina De Angelis, Peter Lesko, Zuzana Orszaghova, Katarina Rejlekova, Maria Reckova, Zuzana Sycova-Mila, Jozef Mardiak, Michal Chovanec, Lucia Kucerova
Journal: Journal of Clinical Oncology (2022)

e17013 Background: Multiple relapsed/refractory GCTs patients have extremely poor prognosis, therefore, new treatment strategies are warranted. Previously, we showed, that cisplatin resistant testicular GCTs overexpress aldehyde-dehydrogenase (ALDH) isoforms and inhibition of ALDH activity by disulfiram is associated with reconstitution of cisplatin sensitivity in vitro as well as in animal model. This study aimed to determine the efficacy and toxicity of ALDH inhibitor disulfiram in combination with cisplatin in patients with multiple relapsed/refractory germ cell cancer. Methods: Twelve patients with multiple relapsed/refractory GCTs were enrolled in the phase II study from May 2019 to September 2021. All patients were pretreated with at least 2 cisplatin-based therapies (median 4, range 2 – 7); 6 tumors (50.0%) were absolutely refractory to cisplatin and 9 patients (75.0%) had visceral non-pulmonary metastases. Disulfiram was administered at a dose of 400 mg daily until progression or unacceptable toxicity, cisplatin was administered at dose 50 mg/m 2 day 1 and 2, every 3 weeks. Twelve evaluable patients had to be enrolled into the first cohort, and if 0 of 12 patients had treatment response, the study was to be terminated. The results of the first stage of the trial are presented in the this report. Results: Median age was 36 years (range: 29 – 48 years). Median number of treatment cycles was 2 (range 1 – 6). During a median follow-up period of 3.1 months (range: 1.3 – 13.9), all (100%) patients experienced disease progression and died. None of patients achieved objective response to treatment, therefore the study was terminated in first stage. Median progression-free survival (PFS) was 1.4 months, 95% CI (0.7 – 1.5 months), and median overall survival (OS) was 2.9 months 95% CI (1.5 – 4.7 months). Disease stabilization for at least 6 months was observed in 2 (16.7%) patients. Treatment was well tolerated, however, 5 (41.7%) of patients experienced grade 3/4 fatigue, 4 (33.3%) thrombocytopenia, 3 (25.0%) anemia, while 2 (16.7%) experienced neutropenia, nausea and infection. Conclusions: This study failed to achieve its primary end point and our data suggest limited efficacy of disulfiram in restoring sensitivity to cisplatin in multiple relapsed/refractory germ cell tumors. Clinical trial information: NCT03950830.
Exosome in ascites can be a potential therapeutic target for gastric cancer with malignant ascites.
Authors: Sujin Hyung, Jihoon Ko, Seung Kim, Won Ki Kang, Jeeyun Lee
Journal: Journal of Clinical Oncology (2022)

e15008 Background: Recently, exosomes widely distributed in body fluids, including blood, urine, saliva, and ascites, serve as an essential biomarker in cancers. Exosomes are considered as a cargo of potential oncogenic protein such as MET during cancer progression. Methods: Exosomes from the malignant ascites (exo Ascites ) of four patients with stage IV gastric cancers (GCs) were isolated by different serial centrifugation steps (300 g, 2000 g, 10,000 g, and 110,000 g at 4). After ultracentrifugation, nanoparticle tracking analysis, western blotting, and immunocytochemistry were performed for characterization of the exo Ascites . Results: In this study, we enrolled 4 patients with malignant ascites which needed to be drained for symptom control. In all 4 patients, we successfully isolated highly concentrated exosomes in 50 ml of ascites (range, 5510 8 – 260 10 8 exosomes/ml). (pt#1, 14010 8 particles/ml; pt#2, 138 10 8 , pt#3, 55.6 10 8 ; pt#4, 260 10 8 exosomes/ml). In twopatients with MET amplification (MET amp ) in tumor specimens, exo Ascites retrieved from the ascites harbored cMET protein in exosomes. In addition, the in-vitro cancer microenvironment model showed that tropism of exo Ascites harboring cMET oncogenic protein had the potential to induce cancer progression (EpCAM immunofluorescent intensity value; a 2.4-fold increase compared to control). In this model, the cancer invasiveness and angiogenesis were significantly enhanced by treatment with exo Ascites with over time. (2.4-(EpCAM immunofluorescent intensity) and 3.6-(CD31 immunofluorescent intensity) fold increase, respectively). Furthermore, the cancer invasiveness was substantially increased as the exosome concentration increased from 0, 10 1 , 10 3 , 10 5 , 10 7 exosome particles/ml. Cancer invasiveness measured by EpCAM immunofluorescent intensity was significantly decreased upon exo siMET treatment in MET amplified GC cells when compared to the control. Conclusions: For the first time, we demonstrated that exosomes from malignant ascites carry MET protein in their exosomes. Exosomes may be early mediators of cancer spread in GC.
Radiomics of MRI scans enables differentiation of benign from malignant soft tissue sarcoma.
Authors: Lin Li, William Kaon, Vidya Sankar Viswanathan, Vijaya K Kosaraju, Christos Kosmas, Robert Devita, Salim Abboud, Anant Madabhushi, Ankit Mangla
Journal: Journal of Clinical Oncology (2022)

11565 Background: Accounting for &gt;15,000 cases annually, soft tissue sarcomas (STSs) represent a large spectrum of tumors with varying histology and clinical course. However, diagnosis of STSs on MRI is challenging due to its heterogeneous imaging phenotypes, such as surrounding soft tissue edema, vascular lesion characteristics, and specific imaging signs, such as splitting of fat between muscles. The goal of this study was to evaluate the utility of radiomic feature analysis for characterizing subtle heterogeneity patterns on T2-weighted MRI to distinguish benign and malignant STSs. Methods: T2-weighted MRI of N = 176 patients with STS were retrospectively obtained from a single institution. (Table) Tumor regions were manually annotated by a radiologist. The radiomic features included textural features, e.g. gray-level co-occurrence matrix features, which quantify the correlation between textural patterns within annotated regions. Maximum relevance minimum redundancy feature selection was employed to identify top 10 radiomic features that are associated with STS malignancy. Then, the identified features were integrated to generate a radiomic risk score (RRS) via logistic regression on a 10-fold cross validation to differentiate benign and malignant STS. Multivariate analysis and the area under the receiver operating characteristic curve (AUC) were employed to evaluate the association of RRS and clinical parameters, including age, gender, and STS location, with STS malignancy. Results: Multivariate analysis illustrated that age and RRS are independent variables in predicting STS malignancy (p &lt; 0.05). The RRS yielded AUC as 0.76 (confidence interval (CI): 0.69-0.83), age 0.71 (CI: 0.64-0.79) and the combination of the RRS and age 0.84 (CI: 0.78-0.90). Conclusions: The RRS generated by radiomic features was found to be a significant predictor of STS malignancy. Furthermore, RRS could provide complementary information to the clinical parameters in differentiating benign and malignant STSs.[Table: see text]
CtDNA dynamic detection reflects the clinical efficacy following treatment in NSCLC patients.
Authors: Wei-neng Feng, Weiguang Gu, Hua Zhang, Yiyu Lu, Weiquan Gu, Minjing Li, Shuang Yang, Zhi-Jian Ye, Jian Liu, Qing Lin, Yaojun Liang, Haiming Chen, Xiaoliang Shi, Dong You
Journal: Journal of Clinical Oncology (2022)

e15032 Background: EGFR is a major oncogene in non-small cell lung cancer (NSCLC) patients. Although EGFR tyrosine kinase inhibitors (TKIs) have effectively improved the survival of NSCLC patients, acquired drug resistance is a critical clinical challenge. Minimal residual disease (MRD) is the direct cause of tumor recurrence, and the abundance of circulating tumor DNA (ctDNA) can be used to reflect the status of MRD. We sought to identify the evaluation of effect of ctDNA on tumor therapeutic efficacy. Methods: Blood samples from 32 advanced NSCLC patients were dynamically collected at different timepoints (0, 4, 8, 12, months) with 1 sampling in 6 patients, 2 samplings in 15 patients, 3 samplings in 7 patients, 4 samplings in 3 patients, and 5 samplings in 1 patient. Targeted sequencing of 450 cancer-related genes were performed for genomic alteration identification at OrigiMed, a College of American Pathologists (CAP) accredited and Clinical Laboratory Improvement Amendments (CLIA) certified laboratory in Shanghai, China. The average variant allele frequency of each sample was used to represent ctDNA abundance. Results: A total of 32 advanced NSCLC patients harboring EGFR sensitive mutations were enrolled in the study. Patients consisted of 12 (37.5%) males and 20 (62.5%) females, with a median age of 60 years old, ranged from 38 to 82 years old. These patients were randomly divided into TKI monotherapy group (N = 11) or TKI treatment combined with pemetrexed group (N = 21). The percentage of stable disease (SD) was more pronounced in patients receiving TKI combined with chemotherapy when compared to patients treated with TKI monotherapy (47.6% [10/21] vs. 36.4% [4/11], respectively). Less patients experienced the progression of disease (PD) when they were treated with TKI combined with chemotherapy than those receiving TKI monotherapy (52.4% [11/21] vs. 63.6% [7/11], respectively). For patients with PD, ctDNA abundance was progressively increased in 55.6% (10/18) during dynamic monitoring; whereas ctDNA abundance kept stable in 44.4% (8/18) patients, while 6/8 patients did not continue to perform ctDNA detection for more than half a year. In patients experienced SD, ctDNA abundance was declined in 78.6% (11/14) during dynamic monitoring, while 21.4% (3/14) patients displayed elevated ctDNA abundance. These 3 patients did not continue to perform ctDNA detection for more than 10 months. In majority of NSCLC patients (65.6% [21/32]), variation of ctDNA abundance was correlated to the treatment-induced clinical response. Conclusions: In conclusion, our results suggested that the variation of ctDNA abundance could promptly reflect the clinical response of treatment. The dynamic variation of ctDNA/MRD can possibly be considered as a biomarker of clinical efficacy following treatment in NSCLC patients.